Finding Extract Method Refactoring Opportunities by Analyzing Development History

Refactoring is an important technique to improve maintainability of software, and developers often use this technique during a development process. Before now, researchers have proposed some techniques finding refactoring opportunities for developers. Finding refactoring opportunities means identifying locations to be refactored. However, there are no specific criteria for developers to determine where they should refactor because the criteria differ from project to project and from developer to developer. In this study, we propose a technique to find refactoring opportunities in source code by using machine learning techniques. Machine learning techniques enable to flexibly find refactoring opportunities by the characteristics of target projects and developers. Our proposed technique learns information on the features of refactorings conducted in the past. Then, based on this information, it suggests some refactorings on given the source code to developers. We investigated three research questions with five open source projects. As a result, we confirmed that the proposed technique was able to find refactorings with high accuracy.